Decentralized AP selection in large-scale wireless LANs considering multi-AP interference
Why this work is in the frame
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Bibliographic record
Abstract
Densification of access points (APs) in wireless local area networks (WLANs) increases the interference and the contention domains of each AP due to multiple overlapped basic service sets (BSSs). Consequently, high interference from multiple co-channel BSS at the target AP impairs system performance. To improve system performance in the presence of multi-BSSs interference, we propose a decentralized AP selection scheme that takes interference at the candidate APs into account and selects AP that offers best signal-interference-plus noise ratio (SINR). In the proposed algorithm, the AP selection process is distributed at the user stations (STAs) and is based on the estimated SINR in the downlink. Estimating SINR in the downlink helps capture the effect of interference from neighboring BSSs or APs. Based on a simulated large-scale 802.11 network, the proposed scheme outperforms the strongest signal first (SSF) AP selection scheme used in current 802.11 standards as well as the mean probe delay (MPD) AP selection algorithm in [3]; it achieves 99% and 43% gains in aggregate throughput over SSF and MPD, respectively. While increasing STA densification, the proposed scheme is shown to increase aggregate network performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it